Artificial Intelligence for Glaucoma: Creating and Implementing Artificial Intelligence for Disease Detection and Progression

Lama A Al-Aswad; Rithambara Ramachandran; Joel S Schuman; Felipe Medeiros; Malvina B Eydelman; Collaborative Community for Ophthalmic Imaging Executive Committee and Glaucoma Workgroup

doi:10.1016/j.ogla.2022.02.010

Artificial Intelligence for Glaucoma: Creating and Implementing Artificial Intelligence for Disease Detection and Progression

Ophthalmol Glaucoma. 2022 Sep-Oct;5(5):e16-e25. doi: 10.1016/j.ogla.2022.02.010. Epub 2022 Feb 24.

Authors

Lama A Al-Aswad¹, Rithambara Ramachandran², Joel S Schuman³, Felipe Medeiros⁴, Malvina B Eydelman⁵; Collaborative Community for Ophthalmic Imaging Executive Committee and Glaucoma Workgroup

Collaborators

Collaborative Community for Ophthalmic Imaging Executive Committee and Glaucoma Workgroup:
Michael D Abramoff, Bhavna J Antony, Michael V Boland, Balwantray C Chauhan, Michael Chiang, Jeffrey L Goldberg, Naama Hammel, Louis R Pasquale, Harry A Quigley, Remo Susanna, Jayme Vianna, Linda Zangwill

Affiliations

¹ Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, New York; Department of Population Health, NYU Langone Health, NYU Grossman School of Medicine, New York, New York. Electronic address: Lama.Al-Aswad@nyulangone.org.
² Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, New York.
³ Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, New York; Department of Biomedical Engineering, New York University Tandon School of Engineering, Brooklyn, New York; Department of Electrical and Computer Engineering, New York University Tandon School of Engineering, Brooklyn, New York; Center for Neural Science, NYU, New York, New York; Neuroscience Institute, NYU Langone Health, New York, New York.
⁴ Department of Ophthalmology, Duke University School of Medicine, Durham, North Carolina; Department of Electrical and Computer Engineering, Pratt School of Engineering, Duke University, Durham, North Carolina.
⁵ US Food and Drug Administration, Silver Spring, Maryland.

Abstract

On September 3, 2020, the Collaborative Community on Ophthalmic Imaging conducted its first 2-day virtual workshop on the role of artificial intelligence (AI) and related machine learning techniques in the diagnosis and treatment of various ophthalmic conditions. In a session entitled "Artificial Intelligence for Glaucoma," a panel of glaucoma specialists, researchers, industry experts, and patients convened to share current research on the application of AI to commonly used diagnostic modalities, including fundus photography, OCT imaging, standard automated perimetry, and gonioscopy. The conference participants focused on the use of AI as a tool for disease prediction, highlighted its ability to address inequalities, and presented the limitations of and challenges to its clinical application. The panelists' discussion addressed AI and health equities from clinical, societal, and regulatory perspectives.

Keywords: Artificial intelligence; Deep learning; Glaucoma; Imaging.

Publication types

Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural

MeSH terms

Artificial Intelligence*
Diagnostic Imaging
Diagnostic Techniques, Ophthalmological
Glaucoma* / diagnosis
Humans
Machine Learning

Grants and funding

R01 EY013178/EY/NEI NIH HHS/United States